metadata
library_name: transformers
pipeline_tag: text-to-speech
tags:
- transformers.js
- mms
- vits
license: cc-by-nc-4.0
datasets:
- ylacombe/google-tamil
language:
- es
Model
This is a finetuned version of the Tamil version of Massively Multilingual Speech (MMS) models, which are light-weight, low-latency TTS models based on the VITS architecture.
It was trained in around 20 minutes with as little as 80 to 150 samples, on this Tamil dataset.
Training recipe available in this github repository: ylacombe/finetune-hf-vits.
Usage
Transformers
from transformers import pipeline
import scipy
model_id = "ylacombe/mms-guj-finetuned-monospeaker"
synthesiser = pipeline("text-to-speech", model_id) # add device=0 if you want to use a GPU
speech = synthesiser("Hola, ¿cómo estás hoy?")
scipy.io.wavfile.write("finetuned_output.wav", rate=speech["sampling_rate"], data=speech["audio"])
Transformers.js
If you haven't already, you can install the Transformers.js JavaScript library from NPM using:
npm i @xenova/transformers
Example: Generate Tamil speech with ylacombe/mms-guj-finetuned-monospeaker.
import { pipeline } from '@xenova/transformers';
// Create a text-to-speech pipeline
const synthesizer = await pipeline('text-to-speech', 'ylacombe/mms-guj-finetuned-monospeaker', {
quantized: false, // Remove this line to use the quantized version (default)
});
// Generate speech
const output = await synthesizer('Hola, ¿cómo estás hoy?');
console.log(output);
// {
// audio: Float32Array(69888) [ ... ],
// sampling_rate: 16000
// }
Optionally, save the audio to a wav file (Node.js):
import wavefile from 'wavefile';
import fs from 'fs';
const wav = new wavefile.WaveFile();
wav.fromScratch(1, output.sampling_rate, '32f', output.audio);
fs.writeFileSync('out.wav', wav.toBuffer());